For a detailed manual for this section please access these links:
Users can access the clinical data download in the TCGA data menu to verify the survival of different groups.
Survival plot menu: Main window.
A csv or R object (rda) file with the clinical information.
Changes the size of the plot
To facilitate visualization and modification of the SummarizedExperiment object, we created this menu in which it is possible to visualize the three matrices of the object (assay matrix [i.e. gene expresssion values], features matrix [i.e. gene information] and sample information matrix). Also, it is possible to download the sample information matrix as a csv file, and, after modifying it, to upload and update the SummarizedExperiment object. This might be useful if for example the user wants to compare two groups not already pre-defined.
The user will be able to perform a Differential methylation regions (DMR) analysis. The output will be a file with the following pattern: DMR_results_GroupCol_group1_group2_pcut_1e-30_meancut_0.55.csv Also, the summarized Experiment will be saved with all the results inside it and the new object will be saved with _result suffix.
Obs: Depending on the number of samples and the number of probes of interest, this analysis can last anywhere from minutes to days. Duration of the analysis also depends on the type of machine and hardware on which it is run.
Differential methylation analysis menu: Main window.
Select a summarized Experiment object (rda)
In this sub-menu the user will be able to plot the results from the Differentially methylated regions (DMR) analysis and the differential expression analysis (DEA).
Volcano plot menu: Main window.
Expected input a csv file with the following pattern:
This box will control the x-axis thresholds “Log FC threshold” for expression and “DNA methylation threshold” for DNA methylation and the y-axis thresholds “P-value adj cut-off”.
Checkbox option:
The option “points to highlight” can perform the following functions:
Change the color of the plot
Change the size of the plot
In this sub-menu the user will be able to plot the mean DNA methylation by groups.
Mean DNA methylation plot menu: Main window.
Expected input is an R object (rda) file with a summarized Experiment object.
Groups column: Select the column that will split the data into groups. This column is selected from the sample matrix (accessed with colData)
x-axis label angle: Change angle of the text in the x-axis
Change the size of the plot
In this sub-menu the user will be able to perform a gene ontology enrichment analysis for the following processes: biological, cellular component, and molecular function. In addition, a network analysis for the groups of genes will be performed.
Select a summarized Experiment object (rda)
Using the TCGAanalyze_Normalization function you can normalize mRNA transcripts and miRNA, using EDASeq package. This function uses Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al. 2011) and between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al. 2010).
Pathway graphs output.
Heatmap plot menu: Main window.
DEA result file should have the following pattern: DEA_result_groupCol_group1_group2_pcut_0.05_logFC.cut_0.csv DMR result file should have the following pattern: DMR_results_groupCol_group1_group2_pcut_0.05_meancut_0.3.csv
Change the size of the plot and the number of bars to plot
To better understand the underlying biological processes, researchers often retrieve a functional profile of a set of genes that might have an important role. This can be done by performing an enrichment analysis.
Given a set of genes that are up-regulated under certain conditions, an enrichment analysis will identify classes of genes or proteins that are over or under-represented using gene set annotations.
Enrichment analysis menu: Main window.
Input a list of genes by:
Change the color of the plot
Change the size of the plot and the number of bars to plot
Inference of gene regulatory networks. Starting with the set of differentially expressed genes, we infer gene regulatory networks using the following state-of-the art inference algorithms: ARACNE(Margolin, Nemenman, and al 2006), CLR(Faith et al. 2007), MRNET(Patrick E Meyer et al. 2007) and C3NET(Altay and Emmert-Streib 2010). These methods are based on mutual inference and use different heuristics to infer the edges in the network. These methods have been made available via Bioconductor/CRAN packages (MINET(Patrick E. Meyer, Lafitte, and Bontempi 2008) and c3net(Altay and Emmert-Streib 2010), respectively).
Using the oncoPrint function from the ComplexHeatmap package, this sub-menu offers a way to visualize multiple genomic alterations.
Oncoprint plot menu: Main window.
Altay, Gökmen, and Frank Emmert-Streib. 2010. “Inferring the Conservative Causal Core of Gene Regulatory Networks.” BMC Systems Biology 4 (1). BioMed Central Ltd: 132.
Bullard, James H, Elizabeth Purdom, Kasper D Hansen, and Sandrine Dudoit. 2010. “Evaluation of Statistical Methods for Normalization and Differential Expression in MRNA-Seq Experiments.” BMC Bioinformatics 11 (1). BioMed Central Ltd: 94.
Faith, Jeremiah J, Boris Hayete, Joshua T Thaden, Ilaria Mogno, Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J Collins, and Timothy S Gardner. 2007. “Large-Scale Mapping and Validation of Escherichia Coli Transcriptional Regulation from a Compendium of Expression Profiles.” PLoS Biol 5 (1): e8.
Luo, Weijun, and Cory Brouwer. 2013. “Pathview: An R/Bioconductor Package for Pathway-Based Data Integration and Visualization.” Bioinformatics 29 (14). Oxford Univ Press: 1830–1.
Margolin, A.A., I. Nemenman, and K. Basso et al. 2006. “ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context.” BMC Bioinformatics 7.
Meyer, Patrick E, Kevin Kontos, Frederic Lafitte, and Gianluca Bontempi. 2007. “Information-Theoretic Inference of Large Transcriptional Regulatory Networks.” EURASIP Journal on Bioinformatics and Systems Biology 2007. Hindawi Publishing Corp.: 8–8.
Meyer, Patrick E., Frédéric Lafitte, and Gianluca Bontempi. 2008. “Minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information.” BMC Bioinformatics 9 (1): 1–10. doi:10.1186/1471-2105-9-461.
Risso, Davide, Katja Schwartz, Gavin Sherlock, and Sandrine Dudoit. 2011. “GC-Content Normalization for Rna-Seq Data.” BMC Bioinformatics 12 (1). BioMed Central Ltd: 480.